Neural Networks for Partially Linear Quantile Regression
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Publication:6626229
DOI10.1080/07350015.2023.2208183zbMATH Open1547.62988MaRDI QIDQ6626229
Publication date: 28 October 2024
Published in: Journal of Business and Economic Statistics (Search for Journal in Brave)
Cites Work
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- Single-index quantile regression
- Inference for single-index quantile regression models with profile optimization
- Semiparametric quantile regression estimation in dynamic models with partially varying coefficients
- Mean squared error properties of kernel estimates of regression quantiles
- Nonparametric estimates of regression quantiles and their local Bahadur representation
- A direct approach to inference in nonparametric and semiparametric quantile models
- Quantile regression in partially linear varying coefficient models
- Additive regression and other nonparametric models
- Non-parametric estimation of conditional quantiles
- Asymptotic behavior of regression quantiles in non-stationary, dependent cases
- Semiparametric least squares (SLS) and weighted SLS estimation of single-index models
- Partially linear modeling of conditional quantiles using penalized splines
- Estimation and variable selection for quantile partially linear single-index models
- Multilayer feedforward networks are universal approximators
- Bivariate tensor-product \(B\)-splines in a partly linear model
- Structure adaptive approach for dimension reduction.
- Weak convergence and empirical processes. With applications to statistics
- A selective overview of deep learning
- Nonparametric regression using deep neural networks with ReLU activation function
- A deep learning semiparametric regression for adjusting complex confounding structures
- Error bounds for approximations with deep ReLU networks
- On deep learning as a remedy for the curse of dimensionality in nonparametric regression
- Conditional quantile processes based on series or many regressors
- Quantile processes for semi and nonparametric regression
- Specification analysis of linear quantile models
- A SINGLE-INDEX QUANTILE REGRESSION MODEL AND ITS ESTIMATION
- Quantile regression estimation of partially linear additive models
- Noncrossing quantile regression curve estimation
- Wild bootstrap for quantile regression
- UNIFORM BIAS STUDY AND BAHADUR REPRESENTATION FOR LOCAL POLYNOMIAL ESTIMATORS OF THE CONDITIONAL QUANTILE FUNCTION
- Regression Quantiles
- EFFICIENT SEMIPARAMETRIC ESTIMATION OF A PARTIALLY LINEAR QUANTILE REGRESSION MODEL
- A Lack-of-Fit Test for Quantile Regression
- Wild residual bootstrap inference for penalized quantile regression with heteroscedastic errors
- Deep learning for finance: deep portfolios
- Neural Network Learning
- GLOBAL BAHADUR REPRESENTATION FOR NONPARAMETRIC CENSORED REGRESSION QUANTILES AND ITS APPLICATIONS
- A Stochastic Approximation Method
- Approximation by superpositions of a sigmoidal function
- Partially linear additive quantile regression in ultra-high dimension
- A SIMPLE NONPARAMETRIC APPROACH FOR ESTIMATION AND INFERENCE OF CONDITIONAL QUANTILE FUNCTIONS
- Deep learning from a statistical perspective
- Semiparametric Quantile Models for Ascending Auctions With Asymmetric Bidders
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